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**some observations on the **

design of early stage clinical trials in the pharmaceutical industry Hans Hockey Biometrics Matters Limited (BML) 13 Nevada Road Hamilton 3216 New Zealand IBC, Kobe, Japan, August 2012 EMR-IBS, Tel Aviv, 23 April 2013

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Structure of talk

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**Structure of talk There is no structure!**

Idle thoughts of a working stiff My background from agricultural research in NZ, Cameroon and Nepal

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**Five Case Studies Two doses plus placebo**

“Factorial” dose escalation and food effect 3-treatment, 3-period cross-over design Escalating dose study with placebo substitution plus Augmented placebo insertion and food effect

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Take-home points Use contrasts that are orthogonal, and are models, not tests Design programs to include more combined studies, including factorial and similar designs Don’t worry about ‘imbalance’ Practical matters can matter more than statistical issues In cross-over designs, never ever fit Sequence Use mixed, not fixed, models for maximum information extraction No particular order

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Take-home point 1 Use contrasts that are orthogonal, and are models, not tests. Design programs to include more combined studies, including factorial and similar designs Don’t worry about ‘imbalance’ Practical matters can matter more than statistical issues In cross-over designs, never ever fit Sequence Use mixed, not fixed, models for maximum information extraction

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Case Study 1 Treatment 5 mg 10 mg Placebo Typical treatment order

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**Case Study 1 Treatment Contrast 5 mg 10 mg Placebo 10 v placebo 1 -1**

1 -1 5 v placebo Two contrasts of interest

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**Case Study 1 Treatment Contrast 5 mg 10 mg Placebo 10 v placebo 1 -1**

1 -1 5 v placebo Dose (mg) 5 10 Linear Quadratic 2 IFF 3 does only!

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**Case Study 1 Treatment Contrast 5 mg 10 mg Placebo 10 v placebo 1 -1**

1 -1 5 v placebo 2 Dose (mg) 5 10 Linear Quadratic Each trt or dose equally useful If first two contrasts, should not you have extra 0 dose/placebo reps? Is this a dose ranging study? Seems not. Is it confirming efficacy and safety of ‘best’ dose. Seems not. What is it?

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**would prefer even more dose levels**

Case Study 1 Treatment Contrast 5 mg 10 mg Placebo 10 v placebo 1 -1 5 v placebo Dose (mg) 5 10 Linear Quadratic 2 A modeller, not a tester, would prefer even more dose levels Lin and Quad orthogonal. First set requires more placebo reps. is linear contrast for 4 equally spaced trts is quadratic contrast Argument against 4 levels, 3 weak first type contrasts. Argument against 3 levels. How do you know you have two good doses but you do not know which is best yet? Should be at stage of 1 dose v placebo, following 4 or 5 or more doses modelling.

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Take-home point 2 Use contrasts that are orthogonal, and are models, not tests Design programs to include more combined studies, including factorial and similar designs Don’t worry about ‘imbalance’ Practical matters can matter more than statistical issues In cross-over designs, never ever fit Sequence Use mixed, not fixed, models for maximum information extraction

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**A typical Phase 1 drug development program list of studies**

Over 50 studies, many are escalating doses or 2x2 crossover designs I was asked to relate XYZ dose to LFT values over whole program.

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**29 studies with XYZ alone and LFTs measured**

Half of a typical(?) phase 1 program

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**Phenytoin+Placebo versus Phenytoin + Vori**

2x2 design? The other half – 26 studies = 55

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Combining studies Ronald Fisher argued in 1926 that "complex" designs (such as factorial designs) were more efficient than studying one factor at a time. Fisher wrote, "No aphorism is more frequently repeated in connection with field trials, than that we must ask Nature few questions, or, ideally, one question, at a time. The writer is convinced that this view is wholly mistaken." Nature, he suggests, will best respond to "a logical and carefully thought out questionnaire". A factorial design allows the effect of several factors and even interactions between them to be determined with the same number of trials as are necessary to determine any one of the effects by itself with the same degree of accuracy. (Wikipedia) In vivo drug-drug interaction studies generally are designed to compare substrate concentrations with and without the interacting drug. Because a specific study can address a number of questions and clinical objectives, many study designs for investigating drug-drug interactions can be considered. In general, crossover designs in which the same subjects receive substrate with and without the interacting drug are more efficient. A study can use a randomized crossover (e.g., S followed by S+I, S+I followed by S), one-sequence crossover (e.g., S followed by S+I), or a parallel (S in one group of subjects and S+I in another group) design, and there may be reason to have another period when the I is removed to assess effect duration. The following possible dosing regimen combinations for a substrate and interacting drug can also be used: single dose/single dose, single dose/multiple dose, multiple dose/single dose, and multiple dose/multiple dose.

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Fed v fasted Escalating dose 204 included 200 mg capsule

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**Combining studies (part 1)**

Case Study 2 Combining studies (part 1)

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A Phase 1 PK study Design 1 Three doses by two food regimes. Use all 6 combinations? Both food regimes at all 3 doses Cohorts Subjects Period 1 2 3 4 1-4 150 fasted 150 fed 5-8 9-12 300 fasted 300 fed 13-16 17-20 600 fasted 600 fed 21-24 Fed v fasted always a 2x2 crossover Too long! (7-day washout three times)

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A Phase 1 PK study Design 2 Three doses by two food regimes. Use all 6 combinations? Both food regimes at all 3 doses Cohorts Subjects Period 1 2 3 4 1-4 150 fasted 150 fed 5-8 9-12 300 fasted 300 fed 13-16 17-20 600 fasted 600 fed 21-24

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A Phase 1 PK study Design 2 Three doses by two food regimes. Use all 6 combinations? Both food regimes at all 3 doses Cohorts Subjects Period 1 2 3 4 1-4 150 fasted 5-8 9-12 300 fasted 300 fed 13-16 17-20 600 fasted 21-24 Still 24 subjects, but 12 per trt now., plus “cross-over”

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A Phase 1 PK study Design 3 Three doses by two food regimes. Use all 6 combinations? Both food regimes at all 3 doses Cohorts Subjects Period 1 2 3 4 1-12 150 fasted 13-18 300 fasted 300 fed 19-24 600 fasted Let all subjects have two treatment periods

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**A Phase 1 PK study – Design 3**

4, not 6, treatments (6 not 12 cells) Only one washout period Safe escalation What assumptions? Two analyses? NO! Note complete confounding in Cohort 1 between period and treatment A Japanese study! Assume no dose x food interaction. Done that, as cross-over. No carryover either with 7 days washout. Assume no period effect from 150 to 600 mg. What 2 analyses? (But should combine.) Unbalanced! Which seques into next talk. And last point about mixed model analysis Ignore fed data Just do 2x2 crossover.

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**Combining studies (part 2)**

Should one 3x3 cross-over study with N subjects replace two 2x2 cross-over studies with 2N subjects total? That is, why not compare A & B & C together instead of A & B separately from A & C? Are three periods too long? Worry that both the A-B and A-C comparisons depend on A treatment being well estimated A typical example: A - market formulation (fasted) B - research formulation (fasted) C - market formulation (fed) Absolutely no interest in B versus C

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**Too separate in time over project?**

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Case Study 3 Capsule v tablet & Fed v fast for capsules

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**A B C Food effect Bioequivalence Why not 2x2? Four periods. Too many?**

Do BIBD of 4 trts over 3 periods. See slide 25 for example.

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**Too separate in time over project?**

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**A1 A2 B1 B2 Food effect (A v B) Formulation effect (1 v 2)**

Why not 2x2? Four periods. Too many? Do BIBD of 4 trts over 3 periods. See slide 25 for example. Not a case study – never seen by me!

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NOT a Case Study Not seen by me anyhow

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Take-home point 3 Use contrasts that are orthogonal, and are models, not tests Design programs to include more combined studies, including factorial and similar designs Don’t worry about ‘imbalance’ Practical matters can matter more than statistical issues In cross-over designs, never ever fit Sequence Use mixed, not fixed, models for maximum information extraction

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Take-home point 3 Use contrasts that are orthogonal, and are models, not tests Design programs to include more combined studies, including factorial and similar designs Not every PK crossover subject needs a placebo Practical matters can matter more than statistical issues In cross-over designs, never ever fit Sequence Use mixed, not fixed, models for maximum information extraction

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Case Study 4 Design: This is a single-blind, placebo-controlled, randomised, “cross-over”, single-dose escalation study in which the toleration, safety and pharmacokinetics of XYZ-123,456 will be investigated. Typical dose escalation design Both ‘cross-over’ and escalation? Two groups or cohorts, 8 each

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**Group A doses (mg) over time**

Subjects Week 1 Week 3 Week 5 Week 7 1&2 Placebo 1 5 20 3&4 5&6 7&8 Placebo insertion, not placebo replacement/substitution (three periods) TGN-1412 – subjects 3&4, 5&6, 7&8 all dosed together. 8 subjects per dose, 8 per Placebo (Same pattern for Group B doses, but 2.5, 10 & 40 mg)

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**Placebo insertion Placebo insertion design**

Subjects Week 1 Week 3 Week 5 Week 7 1&2 Placebo 1 5 20 3&4 5&6 7&8 Placebo insertion design 8 subjects per dose, 8 per Placebo (Same pattern for Group B doses, but 2.5, 10 & 40 mg) Placebo is 25% of subject-periods and same reps as each trt, 8 reps

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**4 subjects per dose, all 6 per Placebo**

Placebo substitution Subjects Week 1 Week 3 Week 5 1&2 Placebo 5 20 3&4 1 5&6 4 subjects per dose, all 6 per Placebo (Placebo is of least interest for PK, but needed for safety comparisons) Placebo is 33% of subject-periods and more reps than each trt. 6 versus 4. Placebo substitution implies less periods and also less subjects.

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**Placebo substitution plus! 6 subjects per dose, all 6 per Placebo**

Why not Placebo substitution plus! Subjects Week 1 Week 3 Week 5 1&2 Placebo 5 20 3&4 1 5&6 7&8 6 subjects per dose, all 6 per Placebo (Placebo is of least interest for PK, but needed for safety comparisons) 6 per treatment! PK versus safety Placebo is 3/12 or 25% again But three periods only And same reps for placebo as trt. 6 each.

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**“¾ placebo substitution”**

Subjects Week 1 Week 3 Week 5 1&2 Placebo 5 20 3&4 1 5&6 7&8 Embrace imbalance!

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**¾ placebo substitution (but it isn’t unbalanced! – it’s a BIBD)**

Subjects Week 1 Week 3 Week 5 ABCD drop BIBD 1&2 5 20 B ACD 3&4 1 C BAD 5&6 D BCA 7&8 A BCD It has general balance. Only drawback – 2 subjects without placebo for safety comparison. Pkists don’t care? Note use of 0 – just another dose level. No ethical issues? “You may receive any of 0, 1, 5, 20 mg in any period” Embrace imbalance! (but it isn’t unbalanced! – it’s a BIBD)

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Take-home point 4 Use contrasts that are orthogonal, and are models, not tests Design programs to include more combined studies, including factorial and similar designs Don’t worry about ‘imbalance’ Practical matters can matter more than statistical issues In cross-over designs, never ever fit Sequence Use mixed, not fixed, models for maximum information extraction

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**Do worry about practical issues**

Case Study 5 Still don’t worry about imbalance (it also is a BIBD, but plus extra replications of non-placebo sequences) Do worry about practical issues (in this case, not sure if 10, 11 or 12 pre-screened subjects will turn up on Day 1, and less on Day 2)

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**Augmented insertion design for 12 subjects over 4 sessions in 2 days**

Dose per day or First Second Sequence AM PM 1 E0 E2 A4 A8 2 12 A0 A2 E4 E8 3 4 5 6 8 7 9 16 10 11 Note fed or fasted over whole day. Ex = Food (fEd) with x capsules Ax = Fasted (fAst) with x capsules (from final protocol)

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**Practical Design Issues**

The design includes placebo insertion such that placebo occurs twice in each of 4 sessions, with double blinding. Each subject has exposure to each active dose, with subjects 9-12 receiving the maximum of two 8-capsule sessions in the one day, after having been exposed to 4 capsules the previous test day. Total exposure ranges from 2 to 16 capsules per day per subject. The design is robust to not having all planned 12 subjects available as there is double replication of the sequences 9 and 10 (sequences 11 and 12). Random allocation of sequences to subjects will be arranged such that if there is a shortfall of 1 or 2 subjects then sequence 12 and then 11 will not be allocated. If there is a further shortfall (very unlikely) then all missing Day 1 subjects will be replaced. All sequences/subjects include the highest dose on Day 2, so given that 10 to 12 subjects completed Day 1, there is no imperative to replace subjects if up to 2 do not attend Day 2. If the Day 2 discontinuation rate is higher though, then consideration will again be given to subject replacement. At least 8 will have the highest dose even if 2 drop out each day.

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Take-home point 5 Use contrasts that are orthogonal, and are models, not tests Design programs to include more combined studies, including factorial and similar designs Don’t worry about ‘imbalance’ Practical matters can matter more than statistical issues In cross-over designs, never ever fit Sequence Use mixed, not fixed, models for maximum information extraction

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**Advantages of cross-overs**

Using within subject variation gives increased precision and power Lower costs (usually extra subjects more expensive than extra periods)

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**Advantages of cross-overs Disadvantage of 2x2 cross-overs**

Using within subject variation gives increased precision and power Lower costs Disadvantage of 2x2 cross-overs Sequence, carryover and treatment.period are all aliased

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**2x2 cross-over ANOVA table (12 subjects)**

d.f. Subjects Sequence 1 Between subject error 10 Total 11 Periods within subjects Treatment Period Within subject error 12 TOTAL 23

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**2x2 cross-over ANOVA table (12 subjects)**

d.f. Subjects Carryover 1 Between subject error 10 Total 11 Periods within subjects Treatment Period Within subject error 12 TOTAL 23

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**2x2 cross-over ANOVA table (12 subjects)**

d.f. Subjects Treatment.Period 1 Between subject error 10 Total 11 Periods within subjects Treatment Period Within subject error 12 TOTAL 23

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**2x2 cross-over ANOVA table (12 subjects)**

d.f. Subjects Sequence 1 Between subject error 9 Total 10 Periods within subjects Treatment Period Within subject error 12 TOTAL 23 Usually see sequence

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**2x2 cross-over ANOVA table (12 subjects)**

d.f. Subjects Sequence 1 Between subject error 9 Total 10 Periods within subjects Treatment Sequence.Treatment Within subject error 12 TOTAL 23 Split-plot has 8 cells, not 4 Not a 2x2x2!

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**My personal preference!**

d.f. Subjects Treatment.Period 1 Between subject error 10 Total 11 Periods within subjects Treatment Period Within subject error 12 TOTAL 23 Why? Sequence not real by definition Carryover is impossible! Makes Grizzle’s two-stage method even harder to justify Helps avoids using sequence in higher order cross-overs.

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Take-home point 6 Use contrasts that are orthogonal, and are models, not tests Design programs to include more combined studies, including factorial and similar designs Don’t worry about ‘imbalance’ Practical matters can matter more than statistical issues In cross-over designs, never ever fit Sequence Use mixed, not fixed, models for maximum information extraction From possibly non-orthogonal designs Is really an analysis point, not design.

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**Any of the classic design texts of the 1950s, such as:**

References Any of the classic design texts of the 1950s, such as: Cochran and Cox (1950) Kempthorne (1952) Cox (1958) Quenouille (1958) and even Fisher (1935) and Yates (1937)! How much experimental design is taught to biostatisticians?

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References (cont’d)

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**Thank you for your attention! **

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